Issue #210 - THE ML ENGINEER 🎅
Alejandro Saucedo
Tech Executive @ Zalando | Chair/Advisor @ UN, EU, ACM, etc | Join 60k+ ML Newsletter
❄️❄️❄️ We wish to thank every single one of our 20k subscribers for your support 💖 and wish you all happy holidays!❄️❄️❄️
This 210 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 15,000+ subscribers. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions 🚀
If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter, 💼 Linkedin and 📕 Facebook!
This week in the MLE #210:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
The history of Modern AI and Deep Learning has an ambiguous and bumpy lineage. Jürgen Schmidhuber has put together a fantastic annotated history timeline highlighting key "most important / relevant" events in the history of neural networks, deep learning, computer science, and mathematics in general, crediting those who laid the foundations of the field.
The ability to data-mine the entirity of github.com can provide absolutely insightful results. The team behind the OSS Insights project has put together a fantstic analysis extracted from crunching the entirity of github.com 2022 data, showcasing insights on top programming languages, repositories, tech trends, activity and more.
It is indeed hard to believe that only a few weeks ago, the OpenAI team released ChatGPT, a state-of-the-art chatbot model that has taken the world by storm. Now OpenAI has done it again with the release of Point-E, a new model for generating 3D models from text. This article from DagsHub provides a fantastic overview of this new model together with an interactive application.
Geoffrey Hinton published the Forward Forward Algorithm last week, an interesting alternative approach to backpropagation which does not require calculating the gradient of the loss function with respect to the network parameters. This codebase provides an intuitive explanation together with an implementation of the algorithms, as well as insightul results against the traditional backprop algorithm.
Developing production machine learning infrastructure is challenging, but finding individuals with the right skills to deliver these systems in an organisation is even harder. Exscientia's MLOps Lead Oleksandr Stasyk has put together insightful thoughts on their journey building MLOps teams and finding the right talent, described as the T-shaped machine learning + engineering + devops skillsets.
Recommended by LinkedIn
Upcoming MLOps Events
The MLOps ecosystem continues to grow at break-neck speeds, making it ever harder for us as practitioners to stay up to date with relevant developments. A fantsatic way to keep on-top of relevant resources is through the great community and events that the MLOps and Production ML ecosystem offers. This is the reason why we have started curating a list of upcoming events in the space, which are outlined below.
Conferences we'll be speaking at:
Other relevant upcoming MLOps conferences:
Open Source MLOps Tools
Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 10,000 ⭐ github stars. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. Four featured libraries in the GPU acceleration space are outlined below.
If you know of any open source and open community events that are not listed do give us a heads up so we can add them!
As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following:
If you know of any guidelines that are not in the "Awesome AI Guidelines" list, please do give us a heads up or feel free to add a pull request!
About us The Institute for Ethical AI & Machine Learning is a UK-based research centre that carries out world-class research into responsible machine learning.